Shoufei Han

Template update is essential for improving the adaptability of tracking algorithms to target appearance variations. While previous methods have leveraged the spatio-temporal complementarity of multi-modal templates for RGBT tracking, a comprehensive analysis of the template update mechanism remains underexplored. In this work, we propose a novel prototype-based framework that decomposes the multi-modal template update process from the perspective of prototype learning into four key components: multi-modal prototype, prototype integration, prototype evaluation, and prototype update algorithm. Our findings highlight that the multi-modal prototype is the most critical factor in enhancing tracking adaptability to appearance variations, leading to more robust target representations. While prototype integration is less crucial when the target representation is already robust, it still contributes to learning a more discriminative representation. Additionally, the accuracy of template updates is strongly influenced by prototype evaluation, which controls the accuracy of the update process. Finally, the prototype update algorithm, which determines when and how template updates occur, is key to maintaining tracking robustness. Building on these insights, we introduce the Multi-modal Prototype RGBT Tracker (MPTrack), which adapts dynamically to appearance variations through prototype learning. MPTrack combines a fixed template from the first frame with both modality-shared and modality-specific templates, forming a robust multi-modal prototype representation. It incorporates a prototype evaluation module that guides updates based on template reliability, and an adaptive update algorithm to manage templates effectively. Additionally, a prototype-guided cross-modal integration module enhances the discriminative power of multi-modal relation modeling. Experimental results on five challenging RGBT tracking benchmarks demonstrate that MPTrack consistently outperforms state-of-the-art methods, setting new performance records. The experimental data and source code will be made publicly available at: https://github.com/mmic-lcl/Datasets-and-benchmark-code.

With the proliferation of massive open online courses, teaching evaluation texts have become an important source of feedback, providing valuable affective and cognitive insights into students’ learning experiences. Although automated sentiment analysis techniques have been developed, existing methods still struggle to capture domain-specific affective knowledge and to systematically model the affective and cognitive processes inherent in educational contexts. To address these challenges, we propose an affective cognition-enhanced graph convolutional network (ACEGCN), which simulates a human-like sentiment reasoning process by integrating domain-specific affective cognition knowledge with syntactic information. Specifically, we construct a domain-specific affective cognition knowledge graph by aligning sentiment entities with a general knowledge base. The resulting knowledge is then integrated into the input sentence to form a knowledge-enriched sentence tree, from which semantically enhanced dependency graphs are constructed using a multigranularity syntactic masking mechanism. Finally, we employ a graph convolutional network to capture key sentiment paths and model complex semantic dependencies. Experimental results across three real-world teaching evaluation datasets demonstrate that ACEGCN significantly outperforms baseline models, suggesting its potential to enhance teaching feedback analysis and help improve teaching quality.

This work studies the problem of block validation in a blockchain network where a block manager acting as a task publisher sends a task (block validation) to all the workers (miners) within the network. The latter carries out the block validation and finally returns the final results to the former. The goal of this work is to maximize the block manager’s profit by jointly optimizing the latency of the block verification process and the reward offered by the manager to miners. Note that the latency and reward are closely coupled. Therefore, in this case, if it is solved directly, they are offered separately, and their dependency is not well considered, leading to overall poor performance. This work formalizes it as an optimization problem considering both delay and reward, and proposes an evolutionary approach, namely, the reborn dandelion algorithm (RDA), to solve it. Specifically, in the proposed algorithm, each individual contains both delays and rewards for different types of miners. A reborn strategy is designed to reborn an individual to replace the worst one in the current population, with the aim of enhancing its exploration ability. A greedy selection strategy is proposed to enhance its exploitation ability. The experimental results on CEC2013 functions and blockchain network instances indicate that our proposed approach is significantly superior to other evolutionary-based ones.

Mining sentiment from user-generated social media content is crucial for fields such as business decision-making and public opinion analysis. Aspect-based sentiment analysis (ABSA) aims to fine-grainedly identify the sentiment of different entities or attributes in texts. However, existing methods face two challenges: 1) attention mechanisms are prone to interference from aspect-irrelevant words, leading to attention bias; and 2) irrelevant syntactic dependencies are frequently introduced by graph convolutional networks constructed upon dense dependency trees, causing semantic interference. To address these challenges, this article proposes a novel method that combines graph transformer with aspect-oriented graph convolutional network (GT-AGCN), reducing attention bias and semantic interference while modeling global semantics and local syntax. First, a context encoder is constructed to capture contextualized semantic representations of the input text sequences. Second, to efficiently model global semantic information, a syntactically constrained Transformer module is developed to alleviate the inherent attention bias. Meanwhile, to accurately capture local syntax information, an aspect-oriented graph convolutional network is designed to suppress interference from aspect-irrelevant terms. Finally, a classifier is utilized to predict the sentiment polarities of target aspects by combining global semantic features with local syntactic features. Empirical evaluations across five widely-adopted ABSA benchmarks demonstrate that GT-AGCN outperforms state-of-the-art baselines, validating the effectiveness of model.